Method and system for fly-by-wire flight control configured for use in electric aircraft

ABSTRACT

In an aspect a system for fly-by-wire flight control configured for use in electric aircraft including at least a sensor, wherein the sensor is communicatively connected a pilot control and configured to detect a pilot input from the pilot control and generate, as a function of the pilot input, command datum. A system includes a flight controller, the flight controller including a computing device and configured to perform a voting algorithm, wherein performing the voting algorithm includes determining that the sensor is an allowed sensor, wherein determining that the sensor is an allowed sensor includes determining that the command datum is an active datum, determining the command datum is an admissible datum, generating, as a function of the command datum and the allowed sensor, a control surface datum wherein the control surface datum is correlated to the pilot input.

FIELD OF THE INVENTION

The present invention generally relates to the field of flight controls.In particular, the present invention is directed to a method and systemfor fly-by-wire flight control configured for use in electric aircraft.

BACKGROUND

In electrically propelled vehicles, such as an electric vertical takeoffand landing (eVTOL) aircraft, it is essential to maintain the integrityof the aircraft until safe landing. In some flights, a component of theaircraft may experience a malfunction or failure which will put theaircraft in an unsafe mode which will compromise the safety of theaircraft, passengers and onboard cargo. A method and system forestimating propulsor output is a necessary component of a safe eVTOLaircraft, and aircraft in general to assess maneuverability andcapabilities of aircraft through flight envelope.

SUMMARY OF THE DISCLOSURE

In an aspect a system for fly-by-wire flight control configured for usein electric aircraft, the system includes at least a sensor, wherein theat least a sensor is communicatively connected to at least a pilotcontrol and configured to detect a pilot input from the at least a pilotcontrol and generate, as a function of the pilot input, at least acommand datum. A system includes a flight controller, the flightcontroller including a computing device and configured to perform avoting algorithm, wherein performing the voting algorithm includesdetermining that the at least a sensor is an allowed sensor, whereindetermining that the at least a sensor is an allowed sensor includesdetermining that the at least a command datum is an active datum,determining the at least a command datum is an admissible datum,generating, as a function of the at least a command datum and theallowed sensor, a control surface datum wherein the control surfacedatum is correlated to the pilot input.

In another aspect a method for fly-by-wire flight control configured foruse in electric aircraft includes detecting, at an at least a sensor, apilot input from at least a pilot control, generating, as a function ofthe pilot input, at least a command datum, determining, at a flightcontroller, as a function of a voting algorithm, that the at least asensor is an allowed sensor, wherein determining includes: determiningthe at least a command datum is an active datum and determining the atleast a command datum is an admissible datum. The method includesgenerating, as a function of the at least a command datum and theallowed sensor, a control surface datum wherein the control surfacedatum is correlated to the pilot input.

These and other aspects and features of non-limiting embodiments of thepresent invention will become apparent to those skilled in the art uponreview of the following description of specific non-limiting embodimentsof the invention in conjunction with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

For the purpose of illustrating the invention, the drawings show aspectsof one or more embodiments of the invention. However, it should beunderstood that the present invention is not limited to the precisearrangements and instrumentalities shown in the drawings, wherein:

FIG. 1 is an exemplary system for fly-by-wire flight control configuredfor use in electric aircraft presented in block diagram form;

FIG. 2 is an exemplary embodiment of a voting algorithm configured foruse in an embodiment of the invention;

FIG. 3 is an exemplary embodiment of a banning algorithm configured foruse in an embodiment of the invention;

FIG. 4 is an exemplary method for fly-by-wire flight control configuredfor use in electric aircraft presented in process flow diagram form;

FIG. 5 is a block diagram of an exemplary embodiment of amachine-learning module;

FIG. 6 is an illustration of an embodiment of an electric aircraft; and

FIG. 7 is a block diagram of a computing system that can be used toimplement any one or more of the methodologies disclosed herein and anyone or more portions thereof.

The drawings are not necessarily to scale and may be illustrated byphantom lines, diagrammatic representations and fragmentary views. Incertain instances, details that are not necessary for an understandingof the embodiments or that render other details difficult to perceivemay have been omitted.

DETAILED DESCRIPTION

At a high level, aspects of the present disclosure are directed tosystems and methods for fly-by-wire flight control configured for use inan electric aircraft. In an embodiment, a system for fly-by-wire flightcontrol configured for use in electric aircraft includes at least asensor, wherein the at least a sensor is communicatively connected to atleast a pilot control and configured to detect a pilot input from the atleast a pilot control and generate, as a function of the pilot input, atleast a command datum. A system includes a flight controller, the flightcontroller including a computing device and configured to perform avoting algorithm, wherein performing the voting algorithm includesdetermining that the at least a sensor is an allowed sensor, whereindetermining that the at least a sensor is an allowed sensor includesdetermining that the at least a command datum is an active datum,determining the at least a command datum is an admissible datum,generating, as a function of the at least a command datum and theallowed sensor, a control surface datum wherein the control surfacedatum is correlated to the pilot input.

Referring now to FIG. 1, exemplary system 100 for fly-by-wire controlconfigured for use in electric aircraft is illustrated in block diagramform. System 100 includes at least a sensor 104. At least a sensor 104is communicatively coupled to at least a pilot control 108.“Communicative coupling”, for the purposes of this disclosure, refers totwo or more components electrically, or otherwise connected andconfigured to transmit and receive signals from one another. Signals mayinclude electrical, electromagnetic, visual, audio, radio waves, oranother undisclosed signal type alone or in combination. At least asensor 104 communicatively connected to at least a pilot control 108 mayinclude a sensor disposed on, near, around or within at least pilotcontrol 108. At least a sensor 104 may include a motion sensor. “Motionsensor”, for the purposes of this disclosure refers to a device orcomponent configured to detect physical movement of an object orgrouping of objects. One of ordinary skill in the art would appreciate,after reviewing the entirety of this disclosure, that motion may includea plurality of types including but not limited to: spinning, rotating,oscillating, gyrating, jumping, sliding, reciprocating, or the like. Atleast a sensor 104 may include, torque sensor, gyroscope, accelerometer,torque sensor, magnetometer, inertial measurement unit (IMU), pressuresensor, force sensor, proximity sensor, displacement sensor, vibrationsensor, among others. At least a sensor 104 may include a sensor suitewhich may include a plurality of sensors that may detect similar orunique phenomena. For example, in a non-limiting embodiment, sensorsuite may include a plurality of accelerometers, a mixture ofaccelerometers and gyroscopes, or a mixture of an accelerometer,gyroscope, and torque sensor. The herein disclosed system and method maycomprise a plurality of sensors in the form of individual sensors or asensor suite working in tandem or individually. A sensor suite mayinclude a plurality of independent sensors, as described herein, whereany number of the described sensors may be used to detect any number ofphysical or electrical quantities associated with an aircraft powersystem or an electrical energy storage system. Independent sensors mayinclude separate sensors measuring physical or electrical quantitiesthat may be powered by and/or in communication with circuitsindependently, where each may signal sensor output to a control circuitsuch as a user graphical interface. In an embodiment, use of a pluralityof independent sensors may result in redundancy configured to employmore than one sensor that measures the same phenomenon, those sensorsbeing of the same type, a combination of, or another type of sensor notdisclosed, so that in the event one sensor fails, the ability to detectphenomenon is maintained and in a non-limiting example, a user alteraircraft usage pursuant to sensor readings. At least a sensor 104 isconfigured to detect pilot input 112 from at least pilot control 108. Atleast pilot control 108 may include a throttle lever, inceptor stick,collective pitch control, steering wheel, brake pedals, pedal controls,toggles, joystick. One of ordinary skill in the art, upon reading theentirety of this disclosure would appreciate the variety of pilot inputcontrols that may be present in an electric aircraft consistent with thepresent disclosure. Inceptor stick may be consistent with disclosure ofinceptor stick in U.S. patent application Ser. No. 17/001,845 and titled“A HOVER AND THRUST CONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”, which isincorporated herein by reference in its entirety. Collective pitchcontrol may be consistent with disclosure of collective pitch control inU.S. patent application Ser. No. 16/929,206 and titled “HOVER AND THRUSTCONTROL ASSEMBLY FOR DUAL-MODE AIRCRAFT”, which is incorporated hereinby reference in its entirety. At least pilot control 108 may bephysically located in the cockpit of the aircraft or remotely locatedoutside of the aircraft in another location communicatively connected toat least a portion of the aircraft. “Communicatively couple”, for thepurposes of this disclosure, is a process whereby one device, component,or circuit is able to receive data from and/or transmit data to anotherdevice, component, or circuit; communicative coupling may be performedby wired or wireless electronic communication, either directly or by wayof one or more intervening devices or components. In an embodiment,communicative coupling includes electrically coupling an output of onedevice, component, or circuit to an input of another device, component,or circuit. Communicative coupling may be performed via a bus or otherfacility for intercommunication between elements of a computing device.Communicative coupling may include indirect connections via “wireless”connection, low power wide area network, radio communication, opticalcommunication, magnetic, capacitive, or optical coupling, or the like.At least pilot control 108 may include buttons, switches, or otherbinary inputs in addition to, or alternatively than digital controlsabout which a plurality of inputs may be received. At least pilotcontrol 108 is configured to receive pilot input 112. Pilot input 112may include a physical manipulation of a control like a pilot using ahand and arm to push or pull a lever, or a pilot using a finger tomanipulate a switch. Pilot input 112 may include a voice command by apilot to a microphone and computing system consistent with the entiretyof this disclosure.

With continued reference to FIG. 1, at least a sensor 104 is configuredto generate, as a function of pilot input 112, command datum 116. A“command datum”, for the purposes of this disclosure, refers anelectronic signal representing at least an element of data correlated topilot input 112 representing a desired change in aircraft conditions asdescribed in the entirety of this disclosure. A “datum”, for thepurposes of this disclosure, refers to at least an element of dataidentifying and/or a pilot input or command. At least pilot control 108may be communicatively connected to any other component presented insystem, the communicative connection may include redundant connectionsconfigured to safeguard against single-point failure. Pilot input 112may indicate a pilot's desire to change the heading or trim of anelectric aircraft. Pilot input 112 may indicate a pilot's desire tochange an aircraft's pitch, roll, yaw, or throttle. “Pitch”, for thepurposes of this disclosure refers to an aircraft's angle of attack,that is the difference between the aircraft's nose and the horizontalflight trajectory. For example, an aircraft pitches “up” when its noseis angled upward compared to horizontal flight, like in a climbmaneuver. In another example, the aircraft pitches “down”, when its noseis angled downward compared to horizontal flight, like in a divemaneuver. “Roll” for the purposes of this disclosure, refers to anaircraft's position about it's longitudinal axis, that is to say thatwhen an aircraft rotates about its axis from its tail to its nose, andone side rolls upward, like in a banking maneuver. “Yaw”, for thepurposes of this disclosure, refers to an aircraft's turn angle, when anaircraft rotates about an imaginary vertical axis intersecting thecenter of the earth and the fuselage of the aircraft. “Throttle”, forthe purposes of this disclosure, refers to an aircraft outputting anamount of thrust from a propulsor. Pilot input 112, when referring tothrottle, may refer to a pilot's desire to increase or decrease thrustproduced by at least a propulsor. Command datum 116 may include anelectrical signal. Electrical signals may include analog signals,digital signals, periodic or aperiodic signal, step signals, unitimpulse signal, unit ramp signal, unit parabolic signal, signumfunction, exponential signal, rectangular signal, triangular signal,sinusoidal signal, sinc function, or pulse width modulated signal. Atleast a sensor 104 may include circuitry, computing devices, electroniccomponents or a combination thereof that translates pilot input 112 intoat least an electronic signal command datum 116 configured to betransmitted to another electronic component.

With continued reference to FIG. 1, system 100 includes flightcontroller 120. Flight controller 120 is communicatively connected to atleast a pilot control 108 and at least a sensor 104. Communicativecoupling may be consistent with any embodiment of communicative couplingas described herein. Flight controller 120 is configured to performvoting algorithm 124. “Flight controller”, for the purposes of thisdisclosure, refers to a component or grouping of components that controltrajectory of the electric aircraft by taking in signals from a pilotand output signals to at least a propulsor and other portions of theelectric aircraft like control surfaces to adjust trajectory. Flightcontroller may mix, refine, adjust, redirect, combine, separate, orperform other types of signal operations to translate pilot desiredtrajectory into aircraft maneuvers. Flight controller, for example, maytake in a pilot input of moving an inceptor stick, the signal from thatmove may be sent to flight controller, which performs any number orcombinations of operations on those signals, then sends out outputsignals to any number of aircraft components that work in tandem orindependently to maneuver the aircraft in response to the pilot input.Flight controller may condition signals such that they can be sent andreceived by various components throughout the electric aircraft.

Additionally, flight controller 120 may include and/or communicate withany computing device, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC). Flight controller may be programmed to operate electronicaircraft to perform at least a flight maneuver; at least a flightmaneuver may include takeoff, landing, stability control maneuvers,emergency response maneuvers, regulation of altitude, roll, pitch, yaw,speed, acceleration, or the like during any phase of flight. At least aflight maneuver may include a flight plan or sequence of maneuvers to beperformed during a flight plan. Flight controller may be designed andconfigured to operate electronic aircraft via fly-by-wire. Flightcontroller is communicatively connected to each propulsor; as usedherein, flight controller is communicatively connected to each propulsorwhere flight controller is able to transmit signals to each propulsorand each propulsor is configured to modify an aspect of propulsorbehavior in response to the signals. As a non-limiting example, flightcontroller may transmit signals to a propulsor via an electrical circuitconnecting flight controller to the propulsor; the circuit may include adirect conductive path from flight controller to propulsor or mayinclude an isolated coupling such as an optical or inductive coupling.Alternatively, or additionally, flight controller may communicate with apropulsor of plurality of propulsors 104 a-n using wirelesscommunication, such as without limitation communication performed usingelectromagnetic radiation including optical and/or radio communication,or communication via magnetic or capacitive coupling. Vehicle controllermay be fully incorporated in an electric aircraft containing a propulsorand may be a remote device operating the electric aircraft remotely viawireless or radio signals, or may be a combination thereof, such as acomputing device in the aircraft configured to perform some steps oractions described herein while a remote device is configured to performother steps. Persons skilled in the art will be aware, after reviewingthe entirety of this disclosure, of many different forms and protocolsof communication that may be used to communicatively couple flightcontroller to propulsors. Persons skilled in the art, upon reviewing theentirety of this disclosure, will be aware of various ways to monitorresistance levels and apply resistance to linear thrust control, as usedand described herein.

Flight controller 120 may include any computing device as described inthis disclosure, including without limitation a microcontroller,microprocessor, digital signal processor (DSP) and/or system on a chip(SoC) as described in this disclosure. Computing device may include, beincluded in, and/or communicate with a mobile device such as a mobiletelephone or smartphone. Fall back flight control system 100 may includea single computing device operating independently, or may include two ormore computing device operating in concert, in parallel, sequentially orthe like; two or more computing devices may be included together in asingle computing device or in two or more computing devices. Flightcontroller 120 may interface or communicate with one or more additionaldevices as described below in further detail via a network interfacedevice. Network interface device may be utilized for connecting flightcontroller 120 to one or more of a variety of networks, and one or moredevices. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network may employ a wiredand/or a wireless mode of communication. In general, any networktopology may be used. Information (e.g., data, software etc.) may becommunicated to and/or from a computer and/or a computing device. Flightcontroller 120 may include but is not limited to, for example, acomputing device or cluster of computing devices in a first location anda second computing device or cluster of computing devices in a secondlocation. Fall back flight control system 100 may include one or morecomputing devices dedicated to data storage, security, distribution oftraffic for load balancing, and the like. Flight controller 120 maydistribute one or more computing tasks as described below across aplurality of computing devices of computing device, which may operate inparallel, in series, redundantly, or in any other manner used fordistribution of tasks or memory between computing devices. Flightcontroller 120 may be implemented using a “shared nothing” architecturein which data is cached at the worker, in an embodiment, this may enablescalability of flight controller 120 and/or computing device.

Flight controller 120 may be designed and/or configured to perform anymethod, method step, or sequence of method steps in any embodimentdescribed in this disclosure, in any order and with any degree ofrepetition. For instance, flight controller 120 may be configured toperform a single step or sequence repeatedly until a desired orcommanded outcome is achieved; repetition of a step or a sequence ofsteps may be performed iteratively and/or recursively using outputs ofprevious repetitions as inputs to subsequent repetitions, aggregatinginputs and/or outputs of repetitions to produce an aggregate result,reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. Flight controller 120may perform any step or sequence of steps as described in thisdisclosure in parallel, such as simultaneously and/or substantiallysimultaneously performing a step two or more times using two or moreparallel threads, processor cores, or the like; division of tasksbetween parallel threads and/or processes may be performed according toany protocol suitable for division of tasks between iterations. Personsskilled in the art, upon reviewing the entirety of this disclosure, willbe aware of various ways in which steps, sequences of steps, processingtasks, and/or data may be subdivided, shared, or otherwise dealt withusing iteration, recursion, and/or parallel processing. Flightcontroller 120, as well as any other component present within disclosedsystems, as well as any other components or combination of componentsmay be connected to a controller area network (CAN) which mayinterconnect all components for signal transmission and reception.

Flight controller 120 is configured to perform a voting algorithm 124,wherein performing voting algorithm 124 includes determining that atleast a sensor 104 is an allowed sensor 128. Voting algorithm 124 mayalso be configured to translate pilot input 112 into commands suitablefor movement of control surfaces mechanically coupled to an electricaircraft. For example, and without limitation, there may be more thanone allowed sensor 128 with associated command datums 116 determined tobe active and admissible. Active and/or admissible command data 116 maybe received by voting algorithm. Voting algorithm may combine activeand/or admissible command data to generate and/or output control surfacedatum 140; combining may include without limitation any form ofmathematical aggregation, such as a sum, a weighted sum, a product, aweighted product, a triangular norm such as a minimum, bounded product,algebraic product, drastic product, or the like, a triangular co-normsuch as a maximum, bounded sum, algebraic sum, drastic sum, or the like,an average such as an arithmetic and/or geometric mean, or the like. Oneof ordinary skill in the art, after reviewing the entirety of thisdisclosure, would appreciate that averaging (finding the mean) of aplurality of command data 116 from a plurality of allowed sensors 128 isonly one example of mathematical or other operations suitable to takeall “votes” into account when generating a control surface datum 140.Allowed sensor 128 includes a sensor that has not been banned by flightcontroller 124. One of ordinary skill in the art would appreciate, afterreviewing the entirety of this disclosure, that any number of flightcontrollers can perform any number of the herein disclosed steps incombination with other computing devices or systems, and perform thesecalculations relating to any number of components, banning and unbanningany component in system 100. Flight controller 120 determines if atleast a sensor 104 is an allowed sensor 104 by determining if commanddatum 116 is an active datum 132. An “active datum”, for the purposes ofthis disclosure, refers to a command received within a predetermined andexpected time limit. For example and without limitation, flightcontroller 120 may calculate when at least a sensor is supposed totransmit command datum 116, and if that command datum 116 arrivesoutside of that time limit or time range, then command datum 116 isdetermined to not be an active datum. If flight controller 120 receivescommand datum 116 within that expected time range, command datum 116 isdetermined to be active datum 132. Active datum 132 is a safeguardagainst old or stale data, wherein stale data may be outdated in view ofmore recent pilot inputs 112. Flight controller 120 performs votingalgorithm 124 in order to determine if command datum 116 is anadmissible datum 136. An “admissible datum”, for the purposes of thisdisclosure, refers to an element of data representing a command to movea control surface relating to the electric aircraft within apredetermined and expected admissible range. An “admissible range”, forthe purposes of this disclosure, refers to a control surface movementcorrelated to an electric aircraft maneuver that is considered safe inview of environmental conditions, aircraft conditions, missionconsiderations, and aircraft power considerations. For example, andwithout limitation, pilot input 112 may be embodied by a pilot moving aninceptor stick to the right, at least a sensor 104 detects that inputand generates command datum 116, command datum is transmitted to anddetermined to be an active datum 132 by flight controller 120. Flightcontroller 120 further takes in information from onboard and offboardsensors that measure environmental conditions like airspeed, angle ofattack, and air density, as well as aircraft conditions like batterylevel. Flight controller 120 then determines, based on command datum 116is within an admissible range based on those parameters. For example,and without limitation, command datum 116 may command an aircraft tobank to the right, but considering environmental conditions likealtitude and propulsor health, command datum 116 may be determined tonot be admissible datum 136. Flight controller 120 may perform votingalgorithm 124 consistent with any voting algorithm described herein.

With continued reference to FIG. 1, flight controller 120 is configuredto ban the at least a sensor 104 that transmitted command datum 116determined to not be active datum 132. A “ban”, for the purposes of thisdisclosure, refers to one or more flight controller's ability to notconsider data from one or more sensors or components determined to notbe transmitting useful and accurate data. For example, and withoutlimitation, flight controller 120 may ban one of at least a sensor 104that does not transmit command datum 116 within a time limit, therebydetermining that the data being transmitted is not trustworthy and doesnot represent pilot input 112 as accurately as possible. Thresholds withwhich flight controller 120 will be discussed with greater detail withreference to FIGS. 2 and 3. Similarly, flight controller 120 isconfigured to ban the at least a sensor 104 transmitted command datum116 determined to not be admissible datum 136. For example, and withoutlimitation, flight controller 120 may determine command datum 116 is notrepresentative of a controls surface movement that correlates to anadmissible range of flight maneuvers given a certain engine poweravailability and air density. Voting algorithm 124 may utilize one ormore machine-learning processes consistent with the entirety of thisdisclosure, and in particular with reference to FIG. 5.

With continued reference to FIG. 1, flight controller 120 is configuredto generate, as a function of the command datum 116 and the allowedsensor 128, control surface datum 140 correlated to pilot input 112.Control surface datum 128 may be an electrical signal consistent withany electrical signal as described in this disclosure. Control surfacedatum 128 may be an electrical signal generated by flight controller 120that is both active and admissible. Control surface datum 140 wouldconstitute one or more command datums 116 that were determined to beboth active datums 132 and admissible datums 136. Control surface datum140 may be the mean of a plurality of control surface datums, commanddatums, active datums, admissible datums, or the like, from any numberof allowed sensors 128. For example, and without limitation, at least asensor 104 includes 10 independent sensors detecting pilot input 112.Two sensors were determined to transmit non-active datums and are thusbanned. Three sensors were determined to transmit non-admissible datumsand are thus banned. The remaining seven allowed sensors would performone or more mathematical operations on their command datums to outputcontrol surface datum 140 that represents a collective value in someway, hence, each sensor that has been allowed has “voted” on what valuecontrol surface datum 140 should be. Control surface datum 140 may be acommand to move an aileron mechanically coupled to electric aircraftconsistent with this disclosure, and with particularity, FIG. 6. Controlsurface datum 140 may be a command to a propulsor mechanically coupledto an electric aircraft, like an electric motor, propeller, combustionengine, or the like, with particular reference to FIG. 6.

With continued reference to FIG. 1, system 100 includes an actuatorwhich is communicatively connected to flight controller 120 and acontrol surface of the aircraft. An actuator may include a computingdevice or plurality of computing devices consistent with the entirety ofthis disclosure. An actuator may be designed and/or configured toperform any method, method step, or sequence of method steps in anyembodiment described in this disclosure, in any order and with anydegree of repetition. For instance, an actuator may be configured toperform a single step or sequence repeatedly until a desired orcommanded outcome is achieved; repetition of a step or a sequence ofsteps may be performed iteratively and/or recursively using outputs ofprevious repetitions as inputs to subsequent repetitions, aggregatinginputs and/or outputs of repetitions to produce an aggregate result,reduction or decrement of one or more variables such as globalvariables, and/or division of a larger processing task into a set ofiteratively addressed smaller processing tasks. An actuator may performany step or sequence of steps as described in this disclosure inparallel, such as simultaneously and/or substantially simultaneouslyperforming a step two or more times using two or more parallel threads,processor cores, or the like; division of tasks between parallel threadsand/or processes may be performed according to any protocol suitable fordivision of tasks between iterations. Persons skilled in the art, uponreviewing the entirety of this disclosure, will be aware of various waysin which steps, sequences of steps, processing tasks, and/or data may besubdivided, shared, or otherwise dealt with using iteration, recursion,and/or parallel processing.

An actuator may include a piston and cylinder system configured toutilize hydraulic pressure to extend and retract a piston coupled to atleast a portion of electric aircraft. An actuator may include a steppermotor or server motor configured to utilize electrical energy intoelectromagnetic movement of a rotor in a stator. An actuator may includea system of gears coupled to an electric motor configured to convertelectrical energy into kinetic energy and mechanical movement through asystem of gears. An actuator may include components, processors,computing devices, or the like configured to detect control surfacedatum 140 flight controller 120. An actuator may be configured toreceive control surface datum 140 from flight controller 120. Anactuator is configured to move at least a portion of the electricaircraft as a function of control surface datum 140. Control surfacedatum 140 indicates a desired change in aircraft heading or thrust,flight controller 120 translates pilot input 112 based on a number ofoperations like voting algorithm 124 into control surface datum 140.That is to say that flight controller 120 is configured to translate apilot input, in the form of moving an inceptor stick, for example, intoelectrical signals to at least an actuator that in turn, moves at leasta portion of the aircraft in a way that manipulates a fluid medium, likeair, to accomplish the pilot's desired maneuver. At least a portion ofthe aircraft that an actuator moves may be a control surface. Anactuator, or any portion of an electric aircraft may include one or moreflight controllers 120 configured to perform any of the operationsdescribed herein and communicate with each of the other flightcontrollers 120 and other portions of an electric aircraft.

Still referring to FIG. 1, an actuator is configured to move controlsurfaces of the aircraft in one or both of its two main modes oflocomotion, or adjust thrust produced at any of the propulsors. Theseelectronic signals can be translated to aircraft control surfaces. Thesecontrol surfaces, in conjunction with forces induced by environment andpropulsion systems, are configured to move the aircraft through a fluidmedium, an example of which is air. A “control surface” as describedherein, is any form of a mechanical linkage with a surface area thatinteracts with forces to move an aircraft. A control surface mayinclude, as a non-limiting example, ailerons, flaps, leading edge flaps,rudders, elevators, spoilers, slats, blades, stabilizers, stabilators,airfoils, a combination thereof, or any other mechanical surface areused to control an aircraft in a fluid medium. Persons skilled in theart, upon reviewing the entirety of this disclosure, will be aware ofvarious mechanical linkages that may be used as a control surface, asused and described in this disclosure. Further, in an embodiment, theactuator may be configured to perform any voting algorithm and/or otheralgorithm as described in the entirety of this disclosure.

In an embodiment, an actuator may be mechanically coupled to a controlsurface at a first end and mechanically coupled to an aircraft at asecond end. As used herein, a person of ordinary skill in the art wouldunderstand “mechanically coupled” to mean that at least a portion of adevice, component, or circuit is connected to at least a portion of theaircraft via a mechanical coupling. Said mechanical coupling caninclude, for example, rigid coupling, such as beam coupling, bellowscoupling, bushed pin coupling, constant velocity, split-muff coupling,diaphragm coupling, disc coupling, donut coupling, elastic coupling,flexible coupling, fluid coupling, gear coupling, grid coupling, hirthjoints, hydrodynamic coupling, jaw coupling, magnetic coupling, Oldhamcoupling, sleeve coupling, tapered shaft lock, twin spring coupling, ragjoint coupling, universal joints, or any combination thereof. In anembodiment, mechanical coupling can be used to connect the ends ofadjacent parts and/or objects of an electric aircraft. Further, in anembodiment, mechanical coupling can be used to join two pieces ofrotating electric aircraft components. Control surfaces may each includeany portion of an aircraft that can be moved or adjusted to affectaltitude, airspeed velocity, groundspeed velocity or direction duringflight. For example, control surfaces may include a component used toaffect the aircrafts' roll and pitch which may comprise one or moreailerons, defined herein as hinged surfaces which form part of thetrailing edge of each wing in a fixed wing aircraft, and which may bemoved via mechanical means such as without limitation servomotors,mechanical linkages, or the like, to name a few. As a further example,control surfaces may include a rudder, which may include, withoutlimitation, a segmented rudder. The rudder may function, withoutlimitation, to control yaw of an aircraft. Also, control surfaces mayinclude other flight control surfaces such as propulsors, rotatingflight controls, or any other structural features which can adjust themovement of the aircraft.

At least a portion of an electric aircraft may include at least apropulsor. A propulsor, as used herein, is a component or device used topropel a craft by exerting force on a fluid medium, which may include agaseous medium such as air or a liquid medium such as water. In anembodiment, when a propulsor twists and pulls air behind it, it will, atthe same time, push an aircraft forward with an equal amount of force.The more air pulled behind an aircraft, the greater the force with whichthe aircraft is pushed forward. Propulsor may include any device orcomponent that consumes electrical power on demand to propel an electricaircraft in a direction or other vehicle while on ground or in-flight.

In an embodiment, at least a portion of the aircraft may include apropulsor, the propulsor may include a propeller, a blade, or anycombination of the two. The function of a propeller is to convert rotarymotion from an engine or other power source into a swirling slipstreamwhich pushes the propeller forwards or backwards. The propulsor mayinclude a rotating power-driven hub, to which are attached severalradial airfoil-section blades such that the whole assembly rotates abouta longitudinal axis. The blade pitch of the propellers may, for example,be fixed, manually variable to a few set positions, automaticallyvariable (e.g. a “constant-speed” type), or any combination thereof. Inan embodiment, propellers for an aircraft are designed to be fixed totheir hub at an angle similar to the thread on a screw makes an angle tothe shaft; this angle may be referred to as a pitch or pitch angle whichwill determine the speed of the forward movement as the blade rotates.

In an embodiment, a propulsor can include a thrust element which may beintegrated into the propulsor. The thrust element may include, withoutlimitation, a device using moving or rotating foils, such as one or morerotors, an airscrew or propeller, a set of airscrews or propellers suchas contra-rotating propellers, a moving or flapping wing, or the like.Further, a thrust element, for example, can include without limitation amarine propeller or screw, an impeller, a turbine, a pump-jet, a paddleor paddle-based device, or the like.

Referring now to FIG. 2, an exemplary embodiment of a voting algorithm200 is presented in block diagram form. Voting algorithm 200 may be thesame or similar to voting algorithm 124, or another voting algorithmaltogether. Voting algorithm 200 includes component 204A-D. Component204A-D may include sensors, sensor suites, flight controllers, computingdevices, electronic component, or other aircraft component as describedherein. For example, and without limitation, component 204A-D, includesfour independent sensors, each of which may be at least a sensor 104.Component 204A would indicate, as an electrical signal or element ofdata, it's ban status 208. A “ban status”, for the purposes of thisdisclosure, refers to a the status of a component within system 100, banstatus 208 may be ‘banned’ or ‘unbanned’. If component 204A is banned,its vote will not be counted, as it is not a sensor whose data is usablefor generation of control surface datum 140. A system that is banned maybe unbanned over multiple iterations of banning algorithm, which will bedisclosed hereinbelow. For example, and without limitation, component204A is not banned, or in other words, the command datum 116 transmittedby component 204A is taken into consideration by voting algorithm 200.Unbanned component 204A then includes active datum status 212. Ifcommand datum 116 is transmitted from an unbanned sensor, hereincomponent 204A, and is transmitted within a predetermined time limit,time range, speed, or in line with another or combination of othertemporal considerations, active datum status 212 is determined. Activedatum status 212 includes whether or not the command datum wastransmitted to flight controller 120 in a temporally appropriate manner.If so, command datum 116 is determined to contain admissible datumstatus 216. Admissible datum status 216 includes whether the commanddatum 116 is an admissible datum, or that it correlates to an admissiblecontrol surface datum. One of ordinary skill in the art wouldappreciate, after reviewing the entirety of this disclosure, that thedetermination of active datum status 212 and admissible datum status 212is not necessarily sequential, that there is any particular order inwhich the determinations are made, that the determinations are madeseparately, that the same computing systems are used in thedeterminations of each status relating to a single component, ormultiple computing systems are used in the determination of statusesrelating to multiple components.

Continuing to refer to FIG. 2, voting algorithm 200, after thedetermination that command datums relating to allowed components 204A-Dare active datums (at active datum status 212) and admissible datums (atadmissible datum status 216), command datums in the form of electricalsignals are transmitted to voter module 220. Voter module 220 may be anycomputing device or component thereof as described in this disclosure.Voter module 220 may include an analog circuit, digital circuit,combinatorial logic circuit, sequential logic circuit and/or anothercircuit suitable for use in an embodiment of the invention. Voter module220 may perform any of the method steps, operations, calculations, orother manipulations of command datums relating to allowed components204A-D. Voter module 220, for example, may receive four command datumsrelating to the change in an aircraft's yaw, as described in thisdisclosure. Voter module 220 may average the command datums and outputthe average as output datum 224. Output datum 224 would therefore be themean of all the command datums associated with each of allowedcomponents 204A-D. Output datum 224 may be the same as or similar tocontrol surface datum 140. Output datum 224 may be transmitted to anyportion of an electric aircraft, including but not limited to computingdevices, flight controllers, signal conditioners, actuators, propulsors,control surfaces, or the like.

Referring now to FIG. 3, banning algorithm 300 is presented in blockdiagram form. Banning algorithm includes current ban status 304. Currentban status 304 may be similar to or the same as any ban status asdescribed herein. Current ban status 304 includes information or one ormore elements of data referring to a component's current status asdetermined by one or more flight controllers 120. Current ban status 304may include a binary value like 1 or 0, indicating currently banned ornot currently banned. Current ban status 304 may include an electricalsignal representing banned or unbanned status. One of ordinary skill inthe art would appreciate, after reviewing the entirety of thisdisclosure, would appreciate the plurality of electrical indications ofa component's current ban status 304 as used in this disclosure. Ifcurrent ban status 304 indicates component is currently banned,currently banned process 308 is initiated. Tolerance datum 316 isdetermined by flight controller 120 as a range of values correspondingto a previously voted on value, such as output datum 224 or controlsurface datum 140. Tolerance datum 316 may be iteratively determined,mathematically manipulated or multiple iterations of a loop, such as ina computer code, or input by one or more personnel. Tolerance datum 316indicates the range of values acceptable in currently banned process 308that the component may be transmitting to continue to be trusted byflight controller 120. For example, and without limitation, if acurrently banned component transmits an electrical signal that does notfall within the previously voted on tolerance datum 316, the tolerancecount re-zero 324 is initiated. Tolerance count re-zero 324 is a statewherein the iterative process of unbanning a banned component is broughtback to zero, making the process start all over again. If a currentlybanned component transmits a datum included in tolerance datum 716, thentolerance count increment 320 is initiated. Tolerance count increment320 increases the tolerance count wherein a currently banned sensor maybe unbanned by provided data that coincides with previously voted ondatums. If tolerance count increment 320 increases past tolerancethreshold 328, then the unban command 332 is initiated. For example, andwithout limitation, tolerance threshold 328 may be five, wherein aniterative process of reading a currently banned component's data must bewithin the threshold five times consecutively before the component isunbanned by unban command 332. Unban command 332 may be transmitted toflight controller 120, or directly to the newly unbanned component, likeat least a sensor 104. To reiterate, and one of ordinary skill in theart would understand, after reviewing the entirety of this disclosure,that only unbanned components may participate in the voting performed byany of the herein described algorithms.

Continuing to refer to FIG. 3, if currently banned status 304 indicatesthe component is currently unbanned, then currently unbanned process 312is initiated. If currently unbanned process 312 is initiated, thenrecent ban status 336 is determined. Recent ban status 336 indicates ifthe component was voted out in a previous iteration of signaltransmission, i.e., the component was not transmitting active andadmissible data consistent with the entirety of this disclosure. Ifcurrently unbanned component transmits data out of tolerance with thepreviously voted on data, vote out count increment 340 is initiated.Vote out count increment 340 indicates an increase in vote out count,the vote out count, if raised above vote out threshold 348, ban command352 is initiated. If currently unbanned component has a recently bannedstatus 336 indicating it has not been recently voted out, then vote outcount decrement 344 is initiated. Vote out count decrement 344 decreasesvote out count, further removing the currently unbanned component frombeing banned by ban command 352, indicating that the currently unbannedcomponent is transmitting usable and accurate data. Currently bannedprocess 308 and currently unbanned process 312 may be repeatedlyperformed before any components are banned or unbanned, performed inperiodic intervals, performed in a specific order, performedsimultaneously, performed on some components at a time, performed on allcomponents simultaneously, among others.

Referring now to FIG. 4, method 400 for fly-by-wire flight controlconfigured for use in electric aircraft is presented in process flowdiagram form. Method 400, at 405 includes detecting, at the at least asensor 104, pilot input 112 from at least a pilot control 108. Pilotcontrol 108 may include a directional control of an electric aircraftsuch as an inceptor stick, pedals, joysticks, steering wheels, amongothers. Pilot control 108 includes a throttle control of an electricaircraft, for example, a gas pedal, electric motor throttle control, orthe like. Pilot control 108 may include any pilot control as describedherein. The at least a sensor 104 may include a motion sensor. Themotion sensor may be any motion sensor as described herein.

Method 400, at step 410, includes generating, as a function of pilotinput 112, at least a command datum 116. Pilot input 112 may be themovement of pilot control 108 indicating a pilot's desire to alteraircraft heading. Pilot input 112 may be any pilot input as describedherein.

Method 400, at step 415, includes determining, at flight controller 120,as a function of voting algorithm 124, that the at least a sensor 104 isan allowed sensor 128. Flight controller 120 may be any flightcontroller as described herein. Allowed sensor 128 may be any sensor asdescribed herein. Voting algorithm 124 may be any voting algorithm asdescribed herein. Voting algorithm 124 may utilize one or moremachine-learning processes.

Method 400, at step 420, includes determining that the at least acommand datum 116 is an active datum. The command datum 116 may be anycommand datum as described herein. The active datum 132 may be anyactive datum as described herein. Flight controller 120 is configured toban the at least a sensor 104 that transmitted a command datum 116determined to not be an active datum 132.

Method 400, at step 425, includes determining that the at least acommand datum 116 is an admissible datum 136. The admissible datum 136may be any admissible datum as described herein. Flight controller 120is configured to ban the at least a sensor 104 that transmitted commanddatum 116 determined to not an admissible datum 136.

Method 400, at step 430, includes generating, as a function of the atleast a command datum 116 and the allowed sensor 128, control surfacedatum 140 correlated to pilot input 112. Allowed sensor 128 may be anyallowed sensor as described herein. Control surface datum 140 may be anycontrol surface datum or equivalent signal as described herein. Controlsurface datum 140 may include the mean of the plurality of includedcommand datums 116 from the plurality of allowed sensors 128. Controlsurface may include an aileron mechanically coupled to an electricaircraft. Control surface may include a propulsor mechanically coupledto an electric aircraft. Control surface includes any control surface asdescribed herein. Propulsor includes any propulsor as described herein.

Referring now to FIG. 5, an exemplary embodiment of a machine-learningmodule 500 that may perform one or more machine-learning processes asdescribed in this disclosure is illustrated. Machine-learning module mayperform determinations, classification, and/or analysis steps, methods,processes, or the like as described in this disclosure using machinelearning processes. A “machine learning process,” as used in thisdisclosure, is a process that automatedly uses training data 504 togenerate an algorithm that will be performed by a computingdevice/module to produce outputs 508 given data provided as inputs 512;this is in contrast to a non-machine learning software program where thecommands to be executed are determined in advance by a user and writtenin a programming language. Machine-learning module may be generated byone or more flight controllers or one or more computing devicesconsistent with the entirety of this disclosure. A generatedmachine-learning module may be used to configure or reconfigure one ormore voting algorithms consistent with the entirety of this disclosureusing software, firmware, and/or configuration or reconfiguration of afield-programmable gate array (FPGA) or other hardware component.Machine-learning module may configure or reconfigure voting algorithmsby tuning one or more coefficients, weights, and/or parameters using invoting algorithms such as tolerance thresholds, vote out thresholds, orother limits within voting algorithm and/or mathematical combinationsperformed for and/or in voting algorithm. For example, and withoutlimitation, machine-learning module may tune a lower coefficient and/orthreshold for a vote out threshold, which may result in a vote outthreshold baseline multiplied by the tuned lower weight, resulting in alesser value for the vote out threshold; that lesser vote out thresholdmay then be more likely to vote out command datums within votingalgorithm.

Still referring to FIG. 5, “training data,” as used herein, is datacontaining correlations that a machine-learning process may use to modelrelationships between two or more categories of data elements. Forinstance, and without limitation, training data 504 may include aplurality of data entries, each entry representing a set of dataelements that were recorded, received, and/or generated together; dataelements may be correlated by shared existence in a given data entry, byproximity in a given data entry, or the like. Multiple data entries intraining data 504 may evince one or more trends in correlations betweencategories of data elements; for instance, and without limitation, ahigher value of a first data element belonging to a first category ofdata element may tend to correlate to a higher value of a second dataelement belonging to a second category of data element, indicating apossible proportional or other mathematical relationship linking valuesbelonging to the two categories. Multiple categories of data elementsmay be related in training data 504 according to various correlations;correlations may indicate causative and/or predictive links betweencategories of data elements, which may be modeled as relationships suchas mathematical relationships by machine-learning processes as describedin further detail below. Training data 504 may be formatted and/ororganized by categories of data elements, for instance by associatingdata elements with one or more descriptors corresponding to categoriesof data elements. As a non-limiting example, training data 504 mayinclude data entered in standardized forms by persons or processes, suchthat entry of a given data element in a given field in a form may bemapped to one or more descriptors of categories. Elements in trainingdata 504 may be linked to descriptors of categories by tags, tokens, orother data elements; for instance, and without limitation, training data504 may be provided in fixed-length formats, formats linking positionsof data to categories such as comma-separated value (CSV) formats and/orself-describing formats such as extensible markup language (XML),JavaScript Object Notation (JSON), or the like, enabling processes ordevices to detect categories of data.

Alternatively or additionally, and continuing to refer to FIG. 5,training data 504 may include one or more elements that are notcategorized; that is, training data 504 may not be formatted or containdescriptors for some elements of data. Machine-learning algorithmsand/or other processes may sort training data 504 according to one ormore categorizations using, for instance, natural language processingalgorithms, tokenization, detection of correlated values in raw data andthe like; categories may be generated using correlation and/or otherprocessing algorithms. As a non-limiting example, in a corpus of text,phrases making up a number “n” of compound words, such as nouns modifiedby other nouns, may be identified according to a statisticallysignificant prevalence of n-grams containing such words in a particularorder; such an n-gram may be categorized as an element of language suchas a “word” to be tracked similarly to single words, generating a newcategory as a result of statistical analysis. Similarly, in a data entryincluding some textual data, a person's name may be identified byreference to a list, dictionary, or other compendium of terms,permitting ad-hoc categorization by machine-learning algorithms, and/orautomated association of data in the data entry with descriptors or intoa given format. The ability to categorize data entries automatedly mayenable the same training data 504 to be made applicable for two or moredistinct machine-learning algorithms as described in further detailbelow. Training data 504 used by machine-learning module 500 maycorrelate any input data as described in this disclosure to any outputdata as described in this disclosure. As a non-limiting illustrativeexample at least a command datum 116 may be input, wherein a controlsurface datum 140 is outputted.

Further referring to FIG. 5, training data may be filtered, sorted,and/or selected using one or more supervised and/or unsupervisedmachine-learning processes and/or models as described in further detailbelow; such models may include without limitation a training dataclassifier 516. Training data classifier 516 may include a “classifier,”which as used in this disclosure is a machine-learning model as definedbelow, such as a mathematical model, neural net, or program generated bya machine learning algorithm known as a “classification algorithm,” asdescribed in further detail below, that sorts inputs into categories orbins of data, outputting the categories or bins of data and/or labelsassociated therewith. A classifier may be configured to output at leasta datum that labels or otherwise identifies a set of data that areclustered together, found to be close under a distance metric asdescribed below, or the like. Machine-learning module 500 may generate aclassifier using a classification algorithm, defined as a processeswhereby a computing device and/or any module and/or component operatingthereon derives a classifier from training data 504. Classification maybe performed using, without limitation, linear classifiers such aswithout limitation logistic regression and/or naive Bayes classifiers,nearest neighbor classifiers such as k-nearest neighbors classifiers,support vector machines, least squares support vector machines, fisher'slinear discriminant, quadratic classifiers, decision trees, boostedtrees, random forest classifiers, learning vector quantization, and/orneural network-based classifiers. As a non-limiting example, trainingdata classifier 516 may classify elements of training data to classes ofdeficiencies, wherein a nourishment deficiency may be categorized to alarge deficiency, a medium deficiency, and/or a small deficiency.

Still referring to FIG. 5, machine-learning module 500 may be configuredto perform a lazy-learning process 520 and/or protocol, which mayalternatively be referred to as a “lazy loading” or “call-when-needed”process and/or protocol, may be a process whereby machine learning isconducted upon receipt of an input to be converted to an output, bycombining the input and training set to derive the algorithm to be usedto produce the output on demand. For instance, an initial set ofsimulations may be performed to cover an initial heuristic and/or “firstguess” at an output and/or relationship. As a non-limiting example, aninitial heuristic may include a ranking of associations between inputsand elements of training data 504. Heuristic may include selecting somenumber of highest-ranking associations and/or training data 504elements. Lazy learning may implement any suitable lazy learningalgorithm, including without limitation a K-nearest neighbors algorithm,a lazy naïve Bayes algorithm, or the like; persons skilled in the art,upon reviewing the entirety of this disclosure, will be aware of variouslazy-learning algorithms that may be applied to generate outputs asdescribed in this disclosure, including without limitation lazy learningapplications of machine-learning algorithms as described in furtherdetail below.

Alternatively or additionally, and with continued reference to FIG. 5,machine-learning processes as described in this disclosure may be usedto generate machine-learning models 524. A “machine-learning model,” asused in this disclosure, is a mathematical and/or algorithmicrepresentation of a relationship between inputs and outputs, asgenerated using any machine-learning process including withoutlimitation any process as described above, and stored in memory; aninput is submitted to a machine-learning model 524 once created, whichgenerates an output based on the relationship that was derived. Forinstance, and without limitation, a linear regression model, generatedusing a linear regression algorithm, may compute a linear combination ofinput data using coefficients derived during machine-learning processesto calculate an output datum. As a further non-limiting example, amachine-learning model 524 may be generated by creating an artificialneural network, such as a convolutional neural network comprising aninput layer of nodes, one or more intermediate layers, and an outputlayer of nodes. Connections between nodes may be created via the processof “training” the network, in which elements from a training data 504set are applied to the input nodes, a suitable training algorithm (suchas Levenberg-Marquardt, conjugate gradient, simulated annealing, orother algorithms) is then used to adjust the connections and weightsbetween nodes in adjacent layers of the neural network to produce thedesired values at the output nodes. This process is sometimes referredto as deep learning.

Still referring to FIG. 5, machine-learning algorithms may include atleast a supervised machine-learning process 528. At least a supervisedmachine-learning process 528, as defined herein, include algorithms thatreceive a training set relating a number of inputs to a number ofoutputs, and seek to find one or more mathematical relations relatinginputs to outputs, where each of the one or more mathematical relationsis optimal according to some criterion specified to the algorithm usingsome scoring function. For instance, a supervised learning algorithm mayinclude at least a command datum 116 as described above as one or moreinputs, one or more control surface datum 140 as outputs, and a scoringfunction representing a desired form of relationship to be detectedbetween inputs and outputs; scoring function may, for instance, seek tomaximize the probability that a given input and/or combination ofelements inputs is associated with a given output to minimize theprobability that a given input is not associated with a given output.Scoring function may be expressed as a risk function representing an“expected loss” of an algorithm relating inputs to outputs, where lossis computed as an error function representing a degree to which aprediction generated by the relation is incorrect when compared to agiven input-output pair provided in training data 504. Persons skilledin the art, upon reviewing the entirety of this disclosure, will beaware of various possible variations of at least a supervisedmachine-learning process 528 that may be used to determine relationbetween inputs and outputs. Supervised machine-learning processes mayinclude classification algorithms as defined above.

Further referring to FIG. 5, machine learning processes may include atleast an unsupervised machine-learning processes 532. An unsupervisedmachine-learning process, as used herein, is a process that derivesinferences in datasets without regard to labels; as a result, anunsupervised machine-learning process may be free to discover anystructure, relationship, and/or correlation provided in the data.Unsupervised processes may not require a response variable; unsupervisedprocesses may be used to find interesting patterns and/or inferencesbetween variables, to determine a degree of correlation between two ormore variables, or the like.

Still referring to FIG. 5, machine-learning module 500 may be designedand configured to create a machine-learning model 524 using techniquesfor development of linear regression models. Linear regression modelsmay include ordinary least squares regression, which aims to minimizethe square of the difference between predicted outcomes and actualoutcomes according to an appropriate norm for measuring such adifference (e.g. a vector-space distance norm); coefficients of theresulting linear equation may be modified to improve minimization.Linear regression models may include ridge regression methods, where thefunction to be minimized includes the least-squares function plus termmultiplying the square of each coefficient by a scalar amount topenalize large coefficients. Linear regression models may include leastabsolute shrinkage and selection operator (LASSO) models, in which ridgeregression is combined with multiplying the least-squares term by afactor of 1 divided by double the number of samples. Linear regressionmodels may include a multi-task lasso model wherein the norm applied inthe least-squares term of the lasso model is the Frobenius normamounting to the square root of the sum of squares of all terms. Linearregression models may include the elastic net model, a multi-taskelastic net model, a least angle regression model, a LARS lasso model,an orthogonal matching pursuit model, a Bayesian regression model, alogistic regression model, a stochastic gradient descent model, aperceptron model, a passive aggressive algorithm, a robustnessregression model, a Huber regression model, or any other suitable modelthat may occur to persons skilled in the art upon reviewing the entiretyof this disclosure. Linear regression models may be generalized in anembodiment to polynomial regression models, whereby a polynomialequation (e.g. a quadratic, cubic or higher-order equation) providing abest predicted output/actual output fit is sought; similar methods tothose described above may be applied to minimize error functions, aswill be apparent to persons skilled in the art upon reviewing theentirety of this disclosure.

Continuing to refer to FIG. 5, machine-learning algorithms may include,without limitation, linear discriminant analysis. Machine-learningalgorithm may include quadratic discriminate analysis. Machine-learningalgorithms may include kernel ridge regression. Machine-learningalgorithms may include support vector machines, including withoutlimitation support vector classification-based regression processes.Machine-learning algorithms may include stochastic gradient descentalgorithms, including classification and regression algorithms based onstochastic gradient descent. Machine-learning algorithms may includenearest neighbors algorithms. Machine-learning algorithms may includeGaussian processes such as Gaussian Process Regression. Machine-learningalgorithms may include cross-decomposition algorithms, including partialleast squares and/or canonical correlation analysis. Machine-learningalgorithms may include naïve Bayes methods. Machine-learning algorithmsmay include algorithms based on decision trees, such as decision treeclassification or regression algorithms. Machine-learning algorithms mayinclude ensemble methods such as bagging meta-estimator, forest ofrandomized tress, AdaBoost, gradient tree boosting, and/or votingclassifier methods. Machine-learning algorithms may include neural netalgorithms, including convolutional neural net processes.

Referring now to FIG. 6, an embodiment of an electric aircraft 600 ispresented. Still referring to FIG. 6, electric aircraft 600 may includea vertical takeoff and landing aircraft (eVTOL). As used herein, avertical take-off and landing (eVTOL) aircraft is one that can hover,take off, and land vertically. An eVTOL, as used herein, is anelectrically powered aircraft typically using an energy source, of aplurality of energy sources to power the aircraft. In order to optimizethe power and energy necessary to propel the aircraft. eVTOL may becapable of rotor-based cruising flight, rotor-based takeoff, rotor-basedlanding, fixed-wing cruising flight, airplane-style takeoff,airplane-style landing, and/or any combination thereof. Rotor-basedflight, as described herein, is where the aircraft generated lift andpropulsion by way of one or more powered rotors coupled with an engine,such as a “quad copter,” multi-rotor helicopter, or other vehicle thatmaintains its lift primarily using downward thrusting propulsors.Fixed-wing flight, as described herein, is where the aircraft is capableof flight using wings and/or foils that generate life caused by theaircraft's forward airspeed and the shape of the wings and/or foils,such as airplane-style flight.

With continued reference to FIG. 6, a number of aerodynamic forces mayact upon the electric aircraft 600 during flight. Forces acting on anelectric aircraft 600 during flight may include, without limitation,thrust, the forward force produced by the rotating element of theelectric aircraft 600 and acts parallel to the longitudinal axis.Another force acting upon electric aircraft 600 may be, withoutlimitation, drag, which may be defined as a rearward retarding forcewhich is caused by disruption of airflow by any protruding surface ofthe electric aircraft 600 such as, without limitation, the wing, rotor,and fuselage. Drag may oppose thrust and acts rearward parallel to therelative wind. A further force acting upon electric aircraft 600 mayinclude, without limitation, weight, which may include a combined loadof the electric aircraft 600 itself, crew, baggage, and/or fuel. Weightmay pull electric aircraft 600 downward due to the force of gravity. Anadditional force acting on electric aircraft 600 may include, withoutlimitation, lift, which may act to oppose the downward force of weightand may be produced by the dynamic effect of air acting on the airfoiland/or downward thrust from the propulsor of the electric aircraft. Liftgenerated by the airfoil may depend on speed of airflow, density of air,total area of an airfoil and/or segment thereof, and/or an angle ofattack between air and the airfoil. For example, and without limitation,electric aircraft 600 are designed to be as lightweight as possible.Reducing the weight of the aircraft and designing to reduce the numberof components is essential to optimize the weight. To save energy, itmay be useful to reduce weight of components of an electric aircraft600, including without limitation propulsors and/or propulsionassemblies. In an embodiment, the motor may eliminate need for manyexternal structural features that otherwise might be needed to join onecomponent to another component. The motor may also increase energyefficiency by enabling a lower physical propulsor profile, reducing dragand/or wind resistance. This may also increase durability by lesseningthe extent to which drag and/or wind resistance add to forces acting onelectric aircraft 600 and/or propulsors.

Referring still to FIG. 6, Aircraft may include at least a verticalpropulsor 604 and at least a forward propulsor 608. A forward propulsoris a propulsor that propels the aircraft in a forward direction. Forwardin this context is not an indication of the propulsor position on theaircraft; one or more propulsors mounted on the front, on the wings, atthe rear, etc. A vertical propulsor is a propulsor that propels theaircraft in a upward direction; one of more vertical propulsors may bemounted on the front, on the wings, at the rear, and/or any suitablelocation. A propulsor, as used herein, is a component or device used topropel a craft by exerting force on a fluid medium, which may include agaseous medium such as air or a liquid medium such as water. At least avertical propulsor 604 is a propulsor that generates a substantiallydownward thrust, tending to propel an aircraft in a vertical directionproviding thrust for maneuvers such as without limitation, verticaltake-off, vertical landing, hovering, and/or rotor-based flight such as“quadcopter” or similar styles of flight.

With continued reference to FIG. 6, at least a forward propulsor 608 asused in this disclosure is a propulsor positioned for propelling anaircraft in a “forward” direction; at least a forward propulsor mayinclude one or more propulsors mounted on the front, on the wings, atthe rear, or a combination of any such positions. At least a forwardpropulsor may propel an aircraft forward for fixed-wing and/or“airplane”-style flight, takeoff, and/or landing, and/or may propel theaircraft forward or backward on the ground. At least a verticalpropulsor 604 and at least a forward propulsor 608 includes a thrustelement. At least a thrust element may include any device or componentthat converts the mechanical energy of a motor, for instance in the formof rotational motion of a shaft, into thrust in a fluid medium. At leasta thrust element may include, without limitation, a device using movingor rotating foils, including without limitation one or more rotors, anairscrew or propeller, a set of airscrews or propellers such ascontrarotating propellers, a moving or flapping wing, or the like. Atleast a thrust element may include without limitation a marine propelleror screw, an impeller, a turbine, a pump-jet, a paddle or paddle-baseddevice, or the like. As another non-limiting example, at least a thrustelement may include an eight-bladed pusher propeller, such as aneight-bladed propeller mounted behind the engine to ensure the driveshaft is in compression. Propulsors may include at least a motormechanically coupled to the at least a first propulsor as a source ofthrust. A motor may include without limitation, any electric motor,where an electric motor is a device that converts electrical energy intomechanical energy, for instance by causing a shaft to rotate. At least amotor may be driven by direct current (DC) electric power; for instance,at least a first motor may include a brushed DC at least a first motor,or the like. At least a first motor may be driven by electric powerhaving varying or reversing voltage levels, such as alternating current(AC) power as produced by an alternating current generator and/orinverter, or otherwise varying power, such as produced by a switchingpower source. At least a first motor may include, without limitation,brushless DC electric motors, permanent magnet synchronous at least afirst motor, switched reluctance motors, or induction motors. Inaddition to inverter and/or a switching power source, a circuit drivingat least a first motor may include electronic speed controllers or othercomponents for regulating motor speed, rotation direction, and/ordynamic braking. Persons skilled in the art, upon reviewing the entiretyof this disclosure, will be aware of various devices that may be used asat least a thrust element.

With continued reference to FIG. 6, during flight, a number of forcesmay act upon the electric aircraft. Forces acting on an aircraft 600during flight may include thrust, the forward force produced by therotating element of the aircraft 600 and acts parallel to thelongitudinal axis. Drag may be defined as a rearward retarding forcewhich is caused by disruption of airflow by any protruding surface ofthe aircraft 600 such as, without limitation, the wing, rotor, andfuselage. Drag may oppose thrust and acts rearward parallel to therelative wind. Another force acting on aircraft 600 may include weight,which may include a combined load of the aircraft 600 itself, crew,baggage and fuel. Weight may pull aircraft 600 downward due to the forceof gravity. An additional force acting on aircraft 600 may include lift,which may act to oppose the downward force of weight and may be producedby the dynamic effect of air acting on the airfoil and/or downwardthrust from at least a propulsor. Lift generated by the airfoil maydepends on speed of airflow, density of air, total area of an airfoiland/or segment thereof, and/or an angle of attack between air and theairfoil.

It is to be noted that any one or more of the aspects and embodimentsdescribed herein may be conveniently implemented using one or moremachines (e.g., one or more computing devices that are utilized as auser computing device for an electronic document, one or more serverdevices, such as a document server, etc.) programmed according to theteachings of the present specification, as will be apparent to those ofordinary skill in the computer art. Appropriate software coding canreadily be prepared by skilled programmers based on the teachings of thepresent disclosure, as will be apparent to those of ordinary skill inthe software art. Aspects and implementations discussed above employingsoftware and/or software modules may also include appropriate hardwarefor assisting in the implementation of the machine executableinstructions of the software and/or software module.

Such software may be a computer program product that employs amachine-readable storage medium. A machine-readable storage medium maybe any medium that is capable of storing and/or encoding a sequence ofinstructions for execution by a machine (e.g., a computing device) andthat causes the machine to perform any one of the methodologies and/orembodiments described herein. Examples of a machine-readable storagemedium include, but are not limited to, a magnetic disk, an optical disc(e.g., CD, CD-R, DVD, DVD-R, etc.), a magneto-optical disk, a read-onlymemory “ROM” device, a random access memory “RAM” device, a magneticcard, an optical card, a solid-state memory device, an EPROM, an EEPROM,and any combinations thereof. A machine-readable medium, as used herein,is intended to include a single medium as well as a collection ofphysically separate media, such as, for example, a collection of compactdiscs or one or more hard disk drives in combination with a computermemory. As used herein, a machine-readable storage medium does notinclude transitory forms of signal transmission.

Such software may also include information (e.g., data) carried as adata signal on a data carrier, such as a carrier wave. For example,machine-executable information may be included as a data-carrying signalembodied in a data carrier in which the signal encodes a sequence ofinstruction, or portion thereof, for execution by a machine (e.g., acomputing device) and any related information (e.g., data structures anddata) that causes the machine to perform any one of the methodologiesand/or embodiments described herein.

Examples of a computing device include, but are not limited to, anelectronic book reading device, a computer workstation, a terminalcomputer, a server computer, a handheld device (e.g., a tablet computer,a smartphone, etc.), a web appliance, a network router, a networkswitch, a network bridge, any machine capable of executing a sequence ofinstructions that specify an action to be taken by that machine, and anycombinations thereof. In one example, a computing device may includeand/or be included in a kiosk.

FIG. 7 shows a diagrammatic representation of one embodiment of acomputing device in the exemplary form of a computer system 700 withinwhich a set of instructions for causing a control system to perform anyone or more of the aspects and/or methodologies of the presentdisclosure may be executed. It is also contemplated that multiplecomputing devices may be utilized to implement a specially configuredset of instructions for causing one or more of the devices to performany one or more of the aspects and/or methodologies of the presentdisclosure. Computer system 700 includes a processor 704 and a memory708 that communicate with each other, and with other components, via abus 712. Bus 712 may include any of several types of bus structuresincluding, but not limited to, a memory bus, a memory controller, aperipheral bus, a local bus, and any combinations thereof, using any ofa variety of bus architectures.

Processor 704 may include any suitable processor, such as withoutlimitation a processor incorporating logical circuitry for performingarithmetic and logical operations, such as an arithmetic and logic unit(ALU), which may be regulated with a state machine and directed byoperational inputs from memory and/or sensors; processor 704 may beorganized according to Von Neumann and/or Harvard architecture as anon-limiting example. Processor 704 may include, incorporate, and/or beincorporated in, without limitation, a microcontroller, microprocessor,digital signal processor (DSP), Field Programmable Gate Array (FPGA),Complex Programmable Logic Device (CPLD), Graphical Processing Unit(GPU), general purpose GPU, Tensor Processing Unit (TPU), analog ormixed signal processor, Trusted Platform Module (TPM), a floating pointunit (FPU), and/or system on a chip (SoC)

Memory 708 may include various components (e.g., machine-readable media)including, but not limited to, a random-access memory component, a readonly component, and any combinations thereof. In one example, a basicinput/output system 716 (BIOS), including basic routines that help totransfer information between elements within computer system 700, suchas during start-up, may be stored in memory 708. Memory 708 may alsoinclude (e.g., stored on one or more machine-readable media)instructions (e.g., software) 720 embodying any one or more of theaspects and/or methodologies of the present disclosure. In anotherexample, memory 708 may further include any number of program modulesincluding, but not limited to, an operating system, one or moreapplication programs, other program modules, program data, and anycombinations thereof.

Computer system 700 may also include a storage device 724. Examples of astorage device (e.g., storage device 724) include, but are not limitedto, a hard disk drive, a magnetic disk drive, an optical disc drive incombination with an optical medium, a solid-state memory device, and anycombinations thereof. Storage device 724 may be connected to bus 712 byan appropriate interface (not shown). Example interfaces include, butare not limited to, SCSI, advanced technology attachment (ATA), serialATA, universal serial bus (USB), IEEE 1394 (FIREWIRE), and anycombinations thereof. In one example, storage device 724 (or one or morecomponents thereof) may be removably interfaced with computer system 700(e.g., via an external port connector (not shown)). Particularly,storage device 724 and an associated machine-readable medium 728 mayprovide nonvolatile and/or volatile storage of machine-readableinstructions, data structures, program modules, and/or other data forcomputer system 700. In one example, software 720 may reside, completelyor partially, within machine-readable medium 728. In another example,software 720 may reside, completely or partially, within processor 704.

Computer system 700 may also include an input device 732. In oneexample, a user of computer system 700 may enter commands and/or otherinformation into computer system 700 via input device 732. Examples ofan input device 732 include, but are not limited to, an alpha-numericinput device (e.g., a keyboard), a pointing device, a joystick, agamepad, an audio input device (e.g., a microphone, a voice responsesystem, etc.), a cursor control device (e.g., a mouse), a touchpad, anoptical scanner, a video capture device (e.g., a still camera, a videocamera), a touchscreen, and any combinations thereof. Input device 732may be interfaced to bus 712 via any of a variety of interfaces (notshown) including, but not limited to, a serial interface, a parallelinterface, a game port, a USB interface, a FIREWIRE interface, a directinterface to bus 712, and any combinations thereof. Input device 732 mayinclude a touch screen interface that may be a part of or separate fromdisplay 736, discussed further below. Input device 732 may be utilizedas a user selection device for selecting one or more graphicalrepresentations in a graphical interface as described above.

A user may also input commands and/or other information to computersystem 700 via storage device 724 (e.g., a removable disk drive, a flashdrive, etc.) and/or network interface device 740. A network interfacedevice, such as network interface device 740, may be utilized forconnecting computer system 700 to one or more of a variety of networks,such as network 744, and one or more remote devices 748 connectedthereto. Examples of a network interface device include, but are notlimited to, a network interface card (e.g., a mobile network interfacecard, a LAN card), a modem, and any combination thereof. Examples of anetwork include, but are not limited to, a wide area network (e.g., theInternet, an enterprise network), a local area network (e.g., a networkassociated with an office, a building, a campus or other relativelysmall geographic space), a telephone network, a data network associatedwith a telephone/voice provider (e.g., a mobile communications providerdata and/or voice network), a direct connection between two computingdevices, and any combinations thereof. A network, such as network 744,may employ a wired and/or a wireless mode of communication. In general,any network topology may be used. Information (e.g., data, software 720,etc.) may be communicated to and/or from computer system 700 via networkinterface device 740.

Computer system 700 may further include a video display adapter 752 forcommunicating a displayable image to a display device, such as displaydevice 736. Examples of a display device include, but are not limitedto, a liquid crystal display (LCD), a cathode ray tube (CRT), a plasmadisplay, a light emitting diode (LED) display, and any combinationsthereof. Display adapter 752 and display device 736 may be utilized incombination with processor 704 to provide graphical representations ofaspects of the present disclosure. In addition to a display device,computer system 700 may include one or more other peripheral outputdevices including, but not limited to, an audio speaker, a printer, andany combinations thereof. Such peripheral output devices may beconnected to bus 712 via a peripheral interface 756. Examples of aperipheral interface include, but are not limited to, a serial port, aUSB connection, a FIREWIRE connection, a parallel connection, and anycombinations thereof.

The foregoing has been a detailed description of illustrativeembodiments of the invention. Various modifications and additions can bemade without departing from the spirit and scope of this invention.Features of each of the various embodiments described above may becombined with features of other described embodiments as appropriate inorder to provide a multiplicity of feature combinations in associatednew embodiments. Furthermore, while the foregoing describes a number ofseparate embodiments, what has been described herein is merelyillustrative of the application of the principles of the presentinvention. Additionally, although particular methods herein may beillustrated and/or described as being performed in a specific order, theordering is highly variable within ordinary skill to achieve methods,systems, and software according to the present disclosure. Accordingly,this description is meant to be taken only by way of example, and not tootherwise limit the scope of this invention.

Exemplary embodiments have been disclosed above and illustrated in theaccompanying drawings. It will be understood by those skilled in the artthat various changes, omissions and additions may be made to that whichis specifically disclosed herein without departing from the spirit andscope of the present invention.

What is claimed is:
 1. A system for fly-by-wire flight controlconfigured for use in electric aircraft, the system comprising: at leasta sensor, wherein the at least a sensor is communicatively connected toat least a pilot control and configured to: detect a pilot input fromthe at least a pilot control; and generate, as a function of the pilotinput, at least a command datum; a flight controller, the flightcontroller comprising a computing device and configured to perform avoting algorithm, wherein performing the voting algorithm furthercomprises: determining that the at least a sensor is an allowed sensor,wherein determining that the at least a sensor is an allowed sensorincludes: determining that the at least a command datum is an activedatum; and determining the at least a command datum is an admissibledatum; and generating, as a function of the at least a command datum andthe allowed sensor, a control surface datum wherein the control surfacedatum is correlated to the pilot input.
 2. The system of claim 1,wherein the voting algorithm is tuned by one or more machine-learningprocesses.
 3. The system of claim 1, wherein the at least a pilotcontrol includes a directional control of an electric aircraft.
 4. Thesystem of claim 1, wherein the at least a pilot control includes athrottle control of an electric aircraft.
 5. The system of claim 1,wherein the control surface includes an aileron.
 6. The system of claim1, wherein the control surface includes a propulsor mechanically coupledto an electric aircraft.
 7. The system of claim 1, wherein the at leasta sensor includes a motion sensor.
 8. The system of claim 1, wherein thecontrol surface datum includes the mean of the plurality of includedcommand datums from the plurality of allowed sensors.
 9. The system ofclaim 1, wherein the flight controller is configured to ban the at leasta sensor that transmitted a command datum determined to be not an activedatum.
 10. The system of claim 1, wherein the flight controller isconfigured to ban the at least a sensor that transmitted a command datumdetermined to be not an admissible datum.
 11. A method for fly-by-wireflight control configured for use in electric aircraft, the methodcomprising: detecting, at an at least a sensor, a pilot input from atleast a pilot control; generating, as a function of the pilot input, atleast a command datum; determining, at a flight controller, as afunction of a voting algorithm, that the at least a sensor is an allowedsensor, wherein determining includes; determining the at least a commanddatum is an active datum; and determining the at least a command datumis an admissible datum; and generating, as a function of the at least acommand datum and the allowed sensor, a control surface datum whereinthe control surface datum is correlated to the pilot input.
 12. Themethod of claim 11, wherein the voting algorithm is tuned by one or moremachine-learning processes.
 13. The method of claim 11, wherein the atleast a pilot control includes a directional control of an electricaircraft.
 14. The method of claim 11, wherein the at least a pilotcontrol includes a throttle control of an electric aircraft.
 15. Themethod of claim 11, wherein the control surface includes an aileron. 16.The method of claim 11, wherein the control surface includes a propulsormechanically coupled to an electric aircraft.
 17. The method of claim11, wherein the at least a sensor includes a motion sensor.
 18. Themethod of claim 1, wherein the control surface datum includes the meanof the plurality of included command datums from the plurality ofallowed sensors.
 19. The method of claim 11, wherein the flightcontroller is configured to ban the at least a sensor that transmitted acommand datum determined to be not an active datum.
 20. The method ofclaim 11, wherein the flight controller is configured to ban the atleast a sensor that transmitted a command datum determined to be not anadmissible datum.